Multiscale SAR image segmentation method based on semi-supervised learning

TLDR: In this article, a multiscale SAR image segmentation method based on semi-supervised learning is proposed, which overcome the disadvantages of low segmentation accuracy and relatively long operation time of the traditional segmentation methods.

Abstract: The invention discloses a multiscale SAR image segmentation method based on semi-supervised learning, belonging to the technical field of image processing and mainly overcoming the disadvantages of low segmentation accuracy and relatively long operation time of the traditional segmentation methods. The implementation steps are as follows: (1) three-layer wavelet transform and three-layer Contourlet transform are respectively carried out on the images to be segmented to finish image decomposition and a coarse decomposition subband, a sub-coarse decomposition subband and a fine decomposition subband are obtained by merge operation; (2) with respect to the coarse decomposition subband, the method of semi-supervised learning is adopted to finish initial segmentation and obtain the results ofinitial segmentation; and (3) multiscale secondary segmentation based on unsupervised learning is carried out on the results of initial segmentation, the sub-coarse decomposition subband and the finedecomposition subband obtained in step (1) to obtain the final segmentation result. The method improves the accuracy of the segmented images, reduces the misclassification rate and can be used for texture image segmentation, natural image segmentation and medical image segmentation.